arXiv AI
Jul 7
Adaptive Margin RLHF via Preference over Preferences
arXiv:2509. 22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks.
Learning Heterogeneous Preferences
The paper introduces a method for learning heterogeneous, individually conditioned utility functions—termed individuated utility—by leveraging rational choice theory. It presents a multi-stage architecture that estimates these functions from multimodal data and evaluates it on a large dataset of aesthetic judgments about automotive wheel designs. Results show that individuated models outperform universal utility models and foundation baselines, indicating that annotator disagreement reflects meaningful preference diversity.
Related stories
arXiv Machine Learning
Aug 20
To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization
The paper introduces CurriPO, a tree‑structured curriculum that automatically adapts to diverse user reward models in AI alignment tasks. By exploiting the natural hierarchy between easy‑ and hard‑to‑optimize reward models, CurriPO covers a broad user population in a single traversal, reusing previously incorporated reward models. Experiments on personalized continuous control show that CurriPO improves population satisfaction by 1.2–2.1× over the strongest baseline while cutting training time and better serving users traditionally underserved by conventional optimization.
arXiv AI
Sep 1
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
The paper introduces Personalized Group Relative Policy Optimization (P‑GRPO), a new alignment framework for large language models that separates advantage estimation from immediate batch statistics. By normalizing advantages using preference‑group‑specific reward histories instead of the concurrent generation group, P‑GRPO maintains contrastive signals for distinct user preferences. Experiments across various tasks show that P‑GRPO converges faster and yields higher rewards than standard GRPO, improving alignment with heterogeneous human preferences while preserving general capabilities.
Hugging Face Trending Papers
Aug 19
To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization
The paper introduces CurriPO, a tree‑structured curriculum that adapts to diverse user reward models in AI alignment. By automatically building a curriculum that branches and reuses reward models, it addresses the problem of users whose reward models are hard to optimize, a group often underserved by conventional methods. Experiments on personalized continuous control demonstrate that CurriPO improves population satisfaction by 1.2–2.1× over the best baseline while cutting training time.
arXiv AI
Sep 16
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning
MiCRo is a two‑stage framework that improves personalized preference learning for large language models. It first uses a context‑aware mixture model to capture diverse human preferences from large binary preference datasets, then applies an online routing strategy to dynamically adjust mixture weights based on context, reducing ambiguity. Experiments on multiple datasets show that MiCRo captures diverse preferences and enhances downstream personalization.
arXiv AI
Jun 4
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
arXiv:2606. 04284v1 Announce Type: cross Abstract: Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values.
arXiv AI
Jun 24
When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models
arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.
arXiv Machine Learning
Aug 11
Multi-Agent Reinforcement Learning via Agent-Specific Preference
arXiv:2608. 08604v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions.
arXiv AI
Jun 9
A Regret Minimization Framework on Preference Learning in Large Language Models
arXiv:2606. 09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals.
arXiv AI
Jul 21
From Weights to Words: Expressing and Editing Preference Model Inferences in Natural Language
arXiv:2607. 16232v1 Announce Type: cross Abstract: The growing use of statistical learning algorithms to infer human preferences from high-dimensional choice data runs up against a fundamental challenge: choice alternatives typically differ in many ways simultaneously, so it is generally unclear which factors actually drove an observed decision and should be credited as preferences.
arXiv AI
Jun 2
Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning
arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.
arXiv AI
Jul 7
Attention Limited Reward Learning
arXiv:2607. 04590v1 Announce Type: new Abstract: Pairwise human comparisons are a primary interface through which modern AI systems learn human preferences.